Statistical Learning Theory and Applications OpenCourseWare: MIT's Free Graduate Level Class on Modern Statistical Language Theory
This free OpenCourseWare class is intended for graduate students who are pursuing degrees in fields like Computational Neuroscience, Biology or Neurology. 'Statistical Learning Theory and Applications', offered by MIT, delves into the relationship between neurons and data, comparing the human brain's learning methods to computers. Previous studies in advanced mathematics and neurology are recommended.
Statistical Applications and Learning Theory: Course Specifics
| Degree Level | Free | Audio | Video | Downloads |
|---|---|---|---|---|
| Graduate | Yes | No | No | Yes |
| Lectures/Notes | Study Materials | Tests/Quizzes |
|---|---|---|
| Yes | Yes | No |
Statistical Applications and Learning Theory: Course Description
This graduate-level lecture course probes the ways that neurons interact with information and the ways that these interactions can be applied to computer systems. Professor Tomaso Poggio, of MIT, a prolific writer on the subject of computational neuroscience theory, has created a course that's concerned with the practical applications of this theory. In this course, students will take an interdisciplinary approach to a challenging topic. Drawing upon fundamentals of engineering, psychology, computer science and other fields, students will study topics that include bioinformatics, manifold regularization, classification, ranking and computer vision. Poggio helps students to explore the ways that computers can imitate the human brain in order to problem solve, modeling such processes as the absorption and sorting of data through human vision. In order to get the most out of this course and its challenging assignments, students will need access to the MATLAB software program.
Course materials are downloadable as PDF files, and they include lecture summaries, slide shows, suggested readings with some links, homework problems and the course's final project. If you're interested in this class, visit the statistical applications and learning theory course page.
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